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dc.contributor.authorLapets, Andreien_US
dc.contributor.authorJansen, Fredericken_US
dc.contributor.authorAlbab, Kinan Daken_US
dc.contributor.authorIssa, Rawaneen_US
dc.contributor.authorQin, Lucyen_US
dc.contributor.authorVaria, Mayanken_US
dc.contributor.authorBestavros, Azeren_US
dc.coverage.spatialSan Jose, C. A.en_US
dc.date.accessioned2019-09-04T15:03:27Z
dc.date.available2019-09-04T15:03:27Z
dc.date.issued2018-01-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000455345900048&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationAndrei Lapets, Frederick Jansen, Kinan Dak Albab, Rawane Issa, Lucy Qin, Mayank Varia, Azer Bestavros. 2018. "Accessible Privacy-PreservingWeb-Based Data Analysis for Assessing and Addressing Economic Inequalities." PROCEEDINGS OF THE 1ST ACM SIGCAS CONFERENCE ON COMPUTING AND SUSTAINABLE SOCIETIES (COMPASS 2018). 1st ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS). San Jose, CA, 2018-06-20 - 2018-06-22. https://doi.org/10.1145/3209811.3212701
dc.identifier.urihttps://hdl.handle.net/2144/37653
dc.description.abstractAn essential component of initiatives that aim to address pervasive inequalities of any kind is the ability to collect empirical evidence of both the status quo baseline and of any improvement that can be attributed to prescribed and deployed interventions. Unfortunately, two substantial barriers can arise preventing the collection and analysis of such empirical evidence: (1) the sensitive nature of the data itself and (2) a lack of technical sophistication and infrastructure available to both an initiative's beneficiaries and to those spearheading it. In the last few years, it has been shown that a cryptographic primitive called secure multi-party computation (MPC) can provide a natural technological resolution to this conundrum. MPC allows an otherwise disinterested third party to contribute its technical expertise and resources, to avoid incurring any additional liabilities itself, and (counterintuitively) to reduce the level of data exposure that existing parties must accept to achieve their data analysis goals. However, achieving these benefits requires the deliberate design of MPC tools and frameworks whose level of accessibility to non-technical users with limited infrastructure and expertise is state-of-the-art. We describe our own experiences designing, implementing, and deploying such usable web applications for secure data analysis within the context of two real-world initiatives that focus on promoting economic equality.en_US
dc.format.extent5 p.en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherASSOC COMPUTING MACHINERYen_US
dc.relation.ispartofPROCEEDINGS OF THE 1ST ACM SIGCAS CONFERENCE ON COMPUTING AND SUSTAINABLE SOCIETIES (COMPASS 2018)
dc.subjectScience & technologyen_US
dc.subjectComputer science, interdisciplinary applicationsen_US
dc.subjectGreen & sustainable science & technologyen_US
dc.subjectComputer scienceen_US
dc.subjectSecure multi-party computationen_US
dc.subjectUsabilityen_US
dc.subjectWeb applicationsen_US
dc.titleAccessible privacy-preserving web-based data analysis for assessing and addressing economic inequalitiesen_US
dc.typeConference materialsen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1145/3209811.3212701
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0003-1053-5394 (Lapets, Andrei)
dc.identifier.orcid0000-0003-0798-8835 (Bestavros, Azer)
dc.identifier.mycv393442


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